Prediction of short-acting beta-agonist usage in patients with asthma using temporal-convolutional neural networks.

Changes in short-acting beta-agonist (SABA) use are an important signal of asthma control and risk of asthma exacerbations. Inhaler sensors passively capture SABA use and may provide longitudinal data to identify at-riskpatients. We evaluate the performance of several ML models in predicting daily SABA use for participants with asthma and determine relevant features for predictive accuracy. Participants with self-reported asthma enrolled in a digital health platform (Propeller Health, WI), which included a smartphone application and inhaler sensors that collected the date and time of SABA use. Linear regression, random forests, and temporal convolutional networks (TCN) were applied to predict expected SABA puffs/person/day from SABA usage and environmental triggers. The models were compared with a simple baseline model using explained variance ( Data included 1.2 million days of data from 13 202 participants. A TCN outperformed other models in predicting puff count ( Predicted SABA use may serve as a valuable forward-looking signal to inform early clinical intervention and self-management. Further validation with known exacerbation events is needed.

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Déclaration de conflit d'intérêts

N.H., A.A., N.M., L.K., and M.B. were employed by ResMed, Inc. at the time of writing. N.H. and N.M. were also employed by Propeller Health at the time of writing. J.S. has no interests to declare.